{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Online learning\n",
    "We need to be able to continuously update our model and adapt to changes in the behavior of the attackers. To do so, we will be building an online learning model.\n",
    "\n",
    "## Setup\n",
    "Import the packages we need and read in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import sqlite3\n",
    "\n",
    "with sqlite3.connect('logs/logs.db') as conn:\n",
    "    logs_2018 = pd.read_sql(\n",
    "        'SELECT * FROM logs WHERE datetime BETWEEN \"2018-01-01\" AND \"2019-01-01\";', \n",
    "        conn, parse_dates=['datetime'], index_col='datetime'\n",
    "    )\n",
    "    hackers_2018 = pd.read_sql(\n",
    "        'SELECT * FROM attacks WHERE start BETWEEN \"2018-01-01\" AND \"2019-01-01\";', \n",
    "        conn, parse_dates=['start', 'end']\n",
    "    ).assign(\n",
    "        start_floor=lambda x: x.start.dt.floor('min'),\n",
    "        end_ceil=lambda x: x.end.dt.ceil('min')\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our functions for getting the X and y for our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_X(log, day):\n",
    "    \"\"\"\n",
    "    Get data we can use for the X\n",
    "    \n",
    "    Parameters:\n",
    "        - log: The logs dataframe\n",
    "        - day: A day or single value we can use as a datetime index slice\n",
    "    \n",
    "    Returns: \n",
    "        A pandas DataFrame\n",
    "    \"\"\"\n",
    "    return pd.get_dummies(log[day].assign(\n",
    "        failures=lambda x: 1 - x.success\n",
    "    ).query('failures > 0').resample('1min').agg(\n",
    "        {'username':'nunique', 'failures': 'sum'}\n",
    "    ).dropna().rename(\n",
    "        columns={'username':'usernames_with_failures'}\n",
    "    ).assign(\n",
    "        day_of_week=lambda x: x.index.dayofweek, \n",
    "        hour=lambda x: x.index.hour\n",
    "    ).drop(columns=['failures']), columns=['day_of_week', 'hour'])\n",
    "\n",
    "def get_y(datetimes, hackers, resolution='1min'):\n",
    "    \"\"\"\n",
    "    Get data we can use for the y (whether or not a hacker attempted a log in during that time).\n",
    "    \n",
    "    Parameters:\n",
    "        - datetimes: The datetimes to check for hackers\n",
    "        - hackers: The dataframe indicating when the attacks started and stopped\n",
    "        - resolution: The granularity of the datetime. Default is 1 minute.\n",
    "        \n",
    "    Returns:\n",
    "        A pandas Series of booleans.\n",
    "    \"\"\"\n",
    "    date_ranges = hackers.apply(\n",
    "        lambda x: pd.date_range(x.start_floor, x.end_ceil, freq=resolution), \n",
    "        axis=1\n",
    "    )\n",
    "    dates = pd.Series()\n",
    "    for date_range in date_ranges:\n",
    "        dates = pd.concat([dates, date_range.to_series()])\n",
    "    return datetimes.isin(dates)\n",
    "\n",
    "def get_X_y(log, day, hackers):\n",
    "    \"\"\"\n",
    "    Get the X, y data to build a model with.\n",
    "    \n",
    "    Parameters:\n",
    "        - log: The logs dataframe\n",
    "        - day: A day or single value we can use as a datetime index slice\n",
    "        - hackers: The dataframe indicating when the attacks started and stopped\n",
    "        \n",
    "    Returns:\n",
    "        X, y tuple where X is a pandas DataFrame and y is a pandas Series\n",
    "    \"\"\"\n",
    "    X = get_X(log, day)\n",
    "    y = get_y(X.reset_index().datetime, hackers)\n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with the 2018 data to initially train our model, and then move to a monthly frequency to predict (and evaluate) each month and update the model afterwards (but before checking the next month)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_2018, y_2018 = get_X_y(logs_2018, '2018', hackers_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stochastic Gradient Descent Classification\n",
    "Using SGD, we will be able to build a logistic regression model that can be updated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\base.py:467: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, y, **fit_params).transform(X)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from ml_utils.partial_fit_pipeline import PartialFitPipeline\n",
    "\n",
    "model = PartialFitPipeline([\n",
    "    ('scale', StandardScaler()), \n",
    "    ('sgd', SGDClassifier(\n",
    "        random_state=0, max_iter=1000, tol=1e-3, loss='log', \n",
    "        average=1000, learning_rate='adaptive', eta0=0.01\n",
    "    ))\n",
    "]).fit(X_2018, y_2018)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As before, we can look at the coefficients for each of our features. The largest coefficient is `usernames_with_failures`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('usernames_with_failures', 1.5344651587934435),\n",
       " ('day_of_week_0', 0.06164314245322163),\n",
       " ('day_of_week_1', -0.026952199196979363),\n",
       " ('day_of_week_2', -0.05023016116182587),\n",
       " ('day_of_week_3', -0.025423230834628722),\n",
       " ('day_of_week_4', -0.03197652065173817),\n",
       " ('day_of_week_5', -0.005999014561309504),\n",
       " ('day_of_week_6', 0.07894278644002553),\n",
       " ('hour_0', 0.13431579778984487),\n",
       " ('hour_1', -0.043450924384377185),\n",
       " ('hour_2', -0.10167150407403336),\n",
       " ('hour_3', -0.07044270179704294),\n",
       " ('hour_4', 0.08923744038825357),\n",
       " ('hour_5', -0.006100526924355815),\n",
       " ('hour_6', 0.029732804125848195),\n",
       " ('hour_7', 0.024513201292794953),\n",
       " ('hour_8', -0.007796800912537199),\n",
       " ('hour_9', -0.08403088314621063),\n",
       " ('hour_10', 0.11196686595127289),\n",
       " ('hour_11', -0.046980848221753004),\n",
       " ('hour_12', -0.025433925615173165),\n",
       " ('hour_13', -0.013031174530186176),\n",
       " ('hour_14', 0.0001413771763088802),\n",
       " ('hour_15', 0.012193749068599156),\n",
       " ('hour_16', -0.014008591558021767),\n",
       " ('hour_17', -0.04443177803318395),\n",
       " ('hour_18', -0.01493215690745455),\n",
       " ('hour_19', 0.037333052263642426),\n",
       " ('hour_20', 0.0570323875628876),\n",
       " ('hour_21', -0.04318098763117806),\n",
       " ('hour_22', 0.06153991279699927),\n",
       " ('hour_23', -0.0425106195612339)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[(col, coef) for col, coef in zip(X_2018.columns, model.named_steps['sgd'].coef_[0])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Predict, Evaluate, and Update Model\n",
    "\n",
    "#### Step 1: Gather test data\n",
    "We will use January 2019:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with sqlite3.connect('logs/logs.db') as conn:\n",
    "    logs_2019 = pd.read_sql(\n",
    "        \"\"\"\n",
    "        SELECT * \n",
    "        FROM logs \n",
    "        WHERE datetime BETWEEN \"2019-01-01\" AND \"2020-01-01\";\n",
    "        \"\"\", conn, parse_dates=['datetime'], index_col='datetime'\n",
    "    )\n",
    "    hackers_2019 = pd.read_sql(\n",
    "        \"\"\"\n",
    "        SELECT * \n",
    "        FROM attacks \n",
    "        WHERE start BETWEEN \"2019-01-01\" AND \"2020-01-01\";\n",
    "        \"\"\", conn, parse_dates=['start', 'end']\n",
    "    ).assign(\n",
    "        start_floor=lambda x: x.start.dt.floor('min'),\n",
    "        end_ceil=lambda x: x.end.dt.ceil('min')\n",
    "    )\n",
    "\n",
    "X_jan, y_jan = get_X_y(logs_2019, '2019-01', hackers_2019)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2: Evaluate Performance\n",
    "Recall is too low here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      1.00      1.00     42549\n",
      "        True       1.00      0.70      0.82        30\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     42579\n",
      "   macro avg       1.00      0.85      0.91     42579\n",
      "weighted avg       1.00      1.00      1.00     42579\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:331: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_jan, model.predict(X_jan)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize where we are on the ROC and precision-recall curves. Let's make a convenience function since we will be plotting these often:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ml_utils.classification import confusion_matrix_visual, plot_pr_curve, plot_roc\n",
    "\n",
    "def plot_performance(model, X, y, threshold=None, title=None, show_target=True):\n",
    "    \"\"\"\n",
    "    Plot the ROC, confusion matrix, and precision-recall curve side-by-side.\n",
    "    \n",
    "    Parameters:\n",
    "        - model: The model object to use for prediction.\n",
    "        - X: The features to pass in for prediction from the testing set.\n",
    "        - y: The actuals to evaluate the prediction.\n",
    "        - threshold: Value to use as threshold when predicting probabilities.\n",
    "        - title: A title for the subplots.\n",
    "        - show_target: Whether to show the target regions on the ROC/PR curve.\n",
    "        \n",
    "    Returns:\n",
    "        Matplotlib Axes.    \n",
    "    \"\"\"\n",
    "    # make the subplots\n",
    "    fig, axes = plt.subplots(1, 3, figsize=(20, 5))\n",
    "\n",
    "    # plot each visualization\n",
    "    plot_roc(y, model.predict_proba(X)[:,1], ax=axes[0])\n",
    "    confusion_matrix_visual(\n",
    "        y, model.predict_proba(X)[:,1] >= (threshold or 0.5), \n",
    "        class_labels=[False, True], ax=axes[1]\n",
    "    )\n",
    "    plot_pr_curve(y, model.predict_proba(X)[:,1], ax=axes[2])\n",
    "\n",
    "    # show the target regions if desired\n",
    "    if show_target:\n",
    "        axes[0].axvspan(0, 0.05, color='palegreen', alpha=0.5)\n",
    "        axes[0].axhspan(0.75, 1, color='paleturquoise', alpha=0.5)\n",
    "        axes[0].annotate(\n",
    "            'region with acceptable FPR and TPR', \n",
    "            xy=(0.05, 0.75), xytext=(0.07, 0.69), \n",
    "            arrowprops=dict(arrowstyle='->')\n",
    "        )\n",
    "\n",
    "        axes[2].axvspan(0.75, 1, color='palegreen', alpha=0.5)\n",
    "        axes[2].axhspan(0.95, 1, color='paleturquoise', alpha=0.5)\n",
    "        axes[2].annotate(\n",
    "            'region with acceptable\\nprecision and recall', \n",
    "            xy=(0.75, 0.95), xytext=(0.3, 0.6), \n",
    "            arrowprops=dict(arrowstyle='->')\n",
    "        )\n",
    "\n",
    "    # show the title if specified\n",
    "    if title:\n",
    "        plt.suptitle(title)\n",
    "\n",
    "    return axes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice we are not quite where we need to be:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.3, 0.8, 'current performance\\n- precision = 100%\\n- recall = 70%')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plot_performance(\n",
    "    model, X_jan, y_jan, \n",
    "    title='Stochastic Gradient Descent Classifier '\\\n",
    "        '(Tested on January 2019 Data)'\n",
    ")\n",
    "axes[0].annotate(\n",
    "    'current performance\\n- FPR = 0%\\n- TPR = 70%', \n",
    "    xy=(0, .7), xytext=(0.05, 0.5), \n",
    "    arrowprops=dict(arrowstyle='->')\n",
    ")\n",
    "\n",
    "axes[2].annotate(\n",
    "    'current performance\\n- precision = 100%\\n- recall = 70%', \n",
    "    xy=(.7, 1), xytext=(0.3, 0.8), \n",
    "    arrowprops=dict(arrowstyle='->')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Try picking a custom threshold based on acceptable TPR and FPR\n",
    "We will be using the threshold with the precision-recall curve, but here is how to get it from the ROC curve:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.011191747078992526"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ml_utils.classification import find_threshold_roc\n",
    "\n",
    "threshold = find_threshold_roc(\n",
    "    y_jan, model.predict_proba(X_jan)[:,1], \n",
    "    fpr_below=0.05, tpr_above=0.75\n",
    ").max()\n",
    "threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Try picking a custom threshold based on acceptable recall and precision\n",
    "Note this is actually the same threshold:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.011191747078992526"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from ml_utils.classification import find_threshold_pr\n",
    "\n",
    "threshold = find_threshold_pr(\n",
    "    y_jan, model.predict_proba(X_jan)[:,1], \n",
    "    min_precision=0.95, min_recall=0.75\n",
    ").max()\n",
    "threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the metrics at this threshold so we can plot where we are on the ROC and precision-recall curves:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve, roc_curve\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_jan, model.predict_proba(X_jan)[:,1])\n",
    "mask = thresholds == threshold\n",
    "fpr_at_threshold, tpr_at_threshold = fpr[mask], tpr[mask]\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_jan, model.predict_proba(X_jan)[:,1])\n",
    "mask = thresholds == threshold\n",
    "# precision and recall have one extra value added at the end for plotting, so we have to remove to use the mask\n",
    "p_at_threshold, r_at_threshold = precision[:-1][mask], recall[:-1][mask]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using this threshold, we are able to move into the acceptable region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.1, 0.75, 'chosen threshold = 1.12%\\n- precision=100.00%\\n- recall=76.67%')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plot_performance(\n",
    "    model, X_jan, y_jan, threshold=threshold, \n",
    "    title='Stochastic Gradient Descent Classifier '\\\n",
    "        '(Tested on January 2019 Data)'\n",
    ")\n",
    "\n",
    "axes[0].annotate(\n",
    "    f'chosen threshold = {threshold:.2%}\\n'\n",
    "    f'- FPR={fpr_at_threshold[0]:.2%}\\n- TPR={tpr_at_threshold[0]:.2%}', \n",
    "    xy=(fpr_at_threshold, tpr_at_threshold), xytext=(0.05, 0.42), \n",
    "    arrowprops=dict(arrowstyle='->')\n",
    ")\n",
    "\n",
    "axes[2].annotate(\n",
    "    f'chosen threshold = {threshold:.2%}\\n'\n",
    "    f'- precision={p_at_threshold[0]:.2%}\\n- recall={r_at_threshold[0]:.2%}', \n",
    "    xy=(r_at_threshold, p_at_threshold), xytext=(0.1, 0.75), \n",
    "    arrowprops=dict(arrowstyle='->')\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4: Update the model to include true labels for previously predicted test data (Jan 2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PartialFitPipeline(memory=None,\n",
       "          steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('sgd', SGDClassifier(alpha=0.0001, average=1000, class_weight=None,\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.01, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='adaptive', loss='log', max_iter=1000,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=0, shuffle=True, tol=0.001,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# update\n",
    "model.partial_fit(X_jan, y_jan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5: Repeat process (this time we use Feb 2019)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      1.00      1.00     39373\n",
      "        True       1.00      0.79      0.88        28\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     39401\n",
      "   macro avg       1.00      0.89      0.94     39401\n",
      "weighted avg       1.00      1.00      1.00     39401\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "X_feb, y_feb = get_X_y(logs_2019, '2019-02', hackers_2019)\n",
    "\n",
    "print(classification_report(y_feb, model.predict_proba(X_feb)[:,1] >= threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the model is better and now a larger section of both the ROC curve and the precision-recall curve fall in the acceptable performance region:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x12D31430>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12D45AB0>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x12D58950>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_performance(\n",
    "    model, X_feb, y_feb, threshold=threshold,\n",
    "    title='Stochastic Gradient Descent Classifier '\\\n",
    "        '(Tested on February 2019 Data)'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Putting our model to the test\n",
    "In the time that we've been building all these models, March 2019 has come and gone. Our stakeholders are done waiting around for us and want results. Let's show our performance.\n",
    "\n",
    "### Step 1: Update with the February 2019 data first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\preprocessing\\data.py:645: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.partial_fit(X, y)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\base.py:464: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  return self.fit(X, **fit_params).transform(X)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "PartialFitPipeline(memory=None,\n",
       "          steps=[('scale', StandardScaler(copy=True, with_mean=True, with_std=True)), ('sgd', SGDClassifier(alpha=0.0001, average=1000, class_weight=None,\n",
       "       early_stopping=False, epsilon=0.1, eta0=0.01, fit_intercept=True,\n",
       "       l1_ratio=0.15, learning_rate='adaptive', loss='log', max_iter=1000,\n",
       "       n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',\n",
       "       power_t=0.5, random_state=0, shuffle=True, tol=0.001,\n",
       "       validation_fraction=0.1, verbose=0, warm_start=False))])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.partial_fit(X_feb, y_feb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2: Predict the March data for the stakeholders and send it off"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    }
   ],
   "source": [
    "X_march, y_march = get_X_y(logs_2019, '2019-03', hackers_2019)\n",
    "march_2019_preds = model.predict_proba(X_march)[:,1] >= threshold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3: Evaluation\n",
    "The subject area experts examined our predictions and returned the following after checking our predictions. We are meeting our metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "       False       1.00      1.00      1.00     41494\n",
      "        True       1.00      0.77      0.87        30\n",
      "\n",
      "   micro avg       1.00      1.00      1.00     41524\n",
      "   macro avg       1.00      0.88      0.93     41524\n",
      "weighted avg       1.00      1.00      1.00     41524\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classification report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_march, march_2019_preds))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We were able to adapt to new data and still meet our performance requirements:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n",
      "c:\\users\\molinstefanie\\packt\\venv\\lib\\site-packages\\sklearn\\pipeline.py:381: DataConversionWarning: Data with input dtype uint8, int64 were all converted to float64 by StandardScaler.\n",
      "  Xt = transform.transform(Xt)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.2, 0.75, 'current performance\\n- precision=100%\\n- recall=77%')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = plot_performance(\n",
    "    model, X_march, y_march, threshold=threshold,\n",
    "    title='Stochastic Gradient Descent Classifier '\\\n",
    "        '(Tested on March 2019 Data)'\n",
    ")\n",
    "\n",
    "axes[0].annotate(\n",
    "    'current performance\\n- FPR=0%\\n- TPR=77%', \n",
    "    xy=(0, 23/30), xytext=(0.05, 0.45), \n",
    "    arrowprops=dict(arrowstyle='->')\n",
    ")\n",
    "axes[2].annotate(\n",
    "    'current performance\\n- precision=100%\\n- recall=77%', \n",
    "    xy=(23/30, 1), xytext=(0.2, 0.75), \n",
    "    arrowprops=dict(arrowstyle='->')\n",
    ")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
